{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四回：文字图例尽眉目"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、Figure和Axes上的文本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib具有广泛的文本支持，包括对数学表达式的支持、对栅格和矢量输出的TrueType支持、具有任意旋转的换行分隔文本以及Unicode支持。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的命令是介绍了通过pyplot API和objected-oriented API分别创建文本的方式。\n",
    "\n",
    "| [`pyplot`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) API | OO API                                                       | description                                                  |\n",
    "| :----------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n",
    "| [`text`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text) | [`text`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text) | 在 [`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的任意位置添加text。 |\n",
    "| [`title`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title) | [`set_title`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title) | 在 [`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)添加title |\n",
    "| [`figtext`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figtext.html#matplotlib.pyplot.figtext) | [`text`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.text) | 在[`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure)的任意位置添加text. |\n",
    "| [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.suptitle.html#matplotlib.pyplot.suptitle) | [`suptitle`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.suptitle) | 在 [`Figure`](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure)添加title |\n",
    "| [`xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel) | [`set_xlabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel) | 在[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的x-axis添加label |\n",
    "| [`ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel) | [`set_ylabel`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel) | 在[`Axes`](https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes)的y-axis添加label |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.text\n",
    "pyplot API：matplotlib.pyplot.text(x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "OO API:Axes.text(self, x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数：  \n",
    "s:此参数是要添加的文本。  \n",
    "xy:此参数是放置文本的点(x，y)。  \n",
    "fontdict:此参数是一个可选参数，并且是一个覆盖默认文本属性的字典。如果fontdict为None，则由rcParams确定默认值。  \n",
    "**返回值**：此方法返回作为创建的文本实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fontdict主要参数具体介绍，更多参数请参考[官网说明](https://matplotlib.org/api/text_api.html?highlight=text#matplotlib.text.Text)：\n",
    "\n",
    "\n",
    "| Property                                                     | Description                                                  |\n",
    "| ------------------------------------------------------------ | :----------------------------------------------------------- |\n",
    "| [`alpha`](https://matplotlib.org/api/_as_gen/matplotlib.artist.Artist.set_alpha.html#matplotlib.artist.Artist.set_alpha) |float or None   该参数指透明度，越接近0越透明，越接近1越不透明   |                           \n",
    "| [`backgroundcolor`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_backgroundcolor) | color  [该参数指文本的背景颜色，具体matplotlib支持颜色如下](https://www.cnblogs.com/charliedaifu/p/9957822.html)                                                    |\n",
    "| [`bbox`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_bbox) | dict with properties for [`patches.FancyBboxPatch`](https://matplotlib.org/api/_as_gen/matplotlib.patches.FancyBboxPatch.html#matplotlib.patches.FancyBboxPatch) 这个是用来设置text周围的box外框 |\n",
    "| [`color`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_color) or c | color 指的是字体的颜色                                                       |\n",
    "| [`fontfamily`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontfamily) or family | {FONTNAME, 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace'} 该参数指的是字体的类型|\n",
    "| [`fontproperties`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontproperties) or font or font_properties | [`font_manager.FontProperties`](https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties) or [`str`](https://docs.python.org/3/library/stdtypes.html#str) or [`pathlib.Path`](https://docs.python.org/3/library/pathlib.html#pathlib.Path) |\n",
    "| [`fontsize`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontsize) or size | float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'} 该参数指字体大小|\n",
    "| [`fontstretch`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontstretch) or stretch | {a numeric value in range 0-1000, 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'} 该参数是指从字体中选择正常、压缩或扩展的字体 |\n",
    "| [`fontstyle`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontstyle) or style | {'normal', 'italic', 'oblique'} 该参数是指字体的样式是否倾斜等                               |\n",
    "| [`fontweight`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_fontweight) or weight | {a numeric value in range 0-1000, 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black'} |         \n",
    "| [`horizontalalignment`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_horizontalalignment) or ha | {'center', 'right', 'left'}  该参数是指选择文本左对齐右对齐还是居中对齐                                |\n",
    "| [`label`](https://matplotlib.org/api/_as_gen/matplotlib.artist.Artist.set_label.html#matplotlib.artist.Artist.set_label) | object                                                       |\n",
    "| [`linespacing`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_linespacing) | float (multiple of font size)                                |\n",
    "| [`position`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_position) | (float, float)                                               |\n",
    "| [`rotation`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_rotation) | float or {'vertical', 'horizontal'} 该参数是指text逆时针旋转的角度，“horizontal”等于0，“vertical”等于90。我们可以根据自己设定来选择合适角度                       |\n",
    "| [`verticalalignment`](https://matplotlib.org/api/text_api.html#matplotlib.text.Text.set_verticalalignment) or va | {'center', 'top', 'bottom', 'baseline', 'center_baseline'}   |\n",
    "                                                    |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fontdict学习的案例\n",
    "#学习的过程中请尝试更换不同的fontdict字典的内容，以便于更好的掌握\n",
    "#---------设置字体样式，分别是字体，颜色，宽度，大小\n",
    "font1 = {'family': 'SimSun',#华文楷体\n",
    "         'alpha':0.7,#透明度\n",
    "        'color':  'purple',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "font2 = {'family': 'Times New Roman',\n",
    "        'color':  'red',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "font3 = {'family': 'serif',\n",
    "        'color':  'blue',\n",
    "        'weight': 'bold',\n",
    "        'size': 14,\n",
    "        }\n",
    "font4 = {'family': 'Calibri',\n",
    "        'color':  'navy',\n",
    "        'weight': 'normal',\n",
    "        'size': 17,\n",
    "        }\n",
    "#-----------四种不同字体显示风格-----\n",
    " \n",
    "#-------建立函数----------\n",
    "x = np.linspace(0.0, 5.0, 100)\n",
    "y = np.cos(2*np.pi*x) * np.exp(-x/3)\n",
    "#-------绘制图像，添加标注----------\n",
    "plt.plot(x, y, '--')\n",
    "plt.title('震荡曲线', fontdict=font1)\n",
    "#------添加文本在指定的坐标处------------\n",
    "plt.text(2, 0.65, r'$\\cos(2 \\pi x) \\exp(-x/3)$', fontdict=font2)\n",
    "#---------设置坐标标签\n",
    "plt.xlabel('Y=time (s)', fontdict=font3)\n",
    "plt.ylabel('X=voltage(mv)', fontdict=font4)\n",
    " \n",
    "# 调整图像边距\n",
    "plt.subplots_adjust(left=0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.title和set_title\n",
    "pyplot API：matplotlib.pyplot.title(label, fontdict=None, loc=None, pad=None, \\*, y=None, \\*\\*kwargs)  \n",
    "OO API:Axes.set_title(self, label, fontdict=None, loc=None, pad=None, \\*, y=None, \\*\\*kwargs)  \n",
    "该命令是用来设置axes的标题。  \n",
    "**参数**：此方法接受以下描述的参数:  \n",
    "label：str，此参数是要添加的文本  \n",
    "fontdict：dict，此参数是控制title文本的外观，默认fontdict如下：\n",
    "```python\n",
    "{'fontsize': rcParams['axes.titlesize'],\n",
    " 'fontweight': rcParams['axes.titleweight'],\n",
    " 'color': rcParams['axes.titlecolor'],\n",
    " 'verticalalignment': 'baseline',\n",
    " 'horizontalalignment': loc}\n",
    " ```\n",
    "loc:str，{'center', 'left', 'right'}默认为center  \n",
    "pad:float,该参数是指标题偏离图表顶部的距离，默认为6。  \n",
    "y:float，该参数是title所在axes垂向的位置。默认值为1，即title位于axes的顶部。  \n",
    "kwargs：该参数是指可以设置的一些奇特文本的属性。  \n",
    "**返回值**：此方法返回作为创建的title实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.figtext和text\n",
    "pyplot API：matplotlib.pyplot.figtext(x, y, s, fontdict=None, \\*\\*kwargs)  \n",
    "OO API:text(self, x, y, s, fontdict=None,\\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:    \n",
    "x,y：float，此参数是指在figure中放置文本的位置。一般取值是在\\[0,1\\]范围内。使用transform关键字可以更改坐标系。  \n",
    "s:str,此参数是指文本  \n",
    "fontdict:dict,此参数是一个可选参数，并且是一个覆盖默认文本属性的字典。如果fontdict为None，则由rcParams确定默认值。  \n",
    "**返回值**：此方法返回作为创建的文本实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.suptitle\n",
    "pyplot API：matplotlib.pyplot.suptitle(t, \\*\\*kwargs)  \n",
    "OO API:suptitle(self, t, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:      \n",
    "t: str,标题的文本  \n",
    "x：float,默认值是0.5.该参数是指文本在figure坐标系下的x坐标  \n",
    "y：float,默认值是0.95.该参数是指文本在figure坐标系下的y坐标  \n",
    "horizontalalignment, ha:该参数是指选择文本水平对齐方式，有三种选择{'center', 'left', right'}，默认值是 'center'  \n",
    "verticalalignment, va：该参数是指选择文本垂直对齐方式，有四种选择{'top', 'center', 'bottom', 'baseline'}，默认值是 'top'  \n",
    "fontsize, size：该参数是指文本的大小，默认值是依据rcParams的设置：rcParams[\"figure.titlesize\"] (default: 'large')  \n",
    "fontweight, weight：该参数是用来设置字重。默认值是依据rcParams的设置：rcParams[\"figure.titleweight\"] (default: 'normal')  \n",
    "[fontproperties](https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties):None or dict,该参数是可选参数，如果该参数被指定，字体的大小将从该参数的默认值中提取。  \n",
    "**返回值**：此方法返回作为创建的title实例的文本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.xlabel和ylabel\n",
    "\n",
    "pyplot API：matplotlib.pyplot.xlabel(xlabel, fontdict=None, labelpad=None, \\*, loc=None, \\*\\*kwargs)  \n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;matplotlib.pyplot.ylabel(ylabel, fontdict=None, labelpad=None,\\*, loc=None, \\*\\*kwargs)  \n",
    "OO API: &emsp;Axes.set_xlabel(self, xlabel, fontdict=None, labelpad=None, \\*, loc=None, \\*\\*kwargs)  \n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;Axes.set_ylabel(self, ylabel, fontdict=None, labelpad=None,\\*, loc=None, \\*\\*kwargs)  \n",
    "**参数**：此方法接受以下描述的参数:        \n",
    "xlabel或者ylabel：label的文本  \n",
    "labelpad:设置label距离轴(axis)的距离  \n",
    "loc:{'left', 'center', 'right'},默认为center  \n",
    "\\*\\*kwargs:[文本](https://matplotlib.org/api/text_api.html#matplotlib.text.Text)属性   \n",
    "**返回值**：此方法返回作为创建的xlabel和ylabel实例的文本。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#文本属性的输入一种是通过**kwargs属性这种方式，一种是通过操作 matplotlib.font_manager.FontProperties 方法\n",
    "#该案例中对于x_label采用**kwargs调整字体属性，y_label则采用 matplotlib.font_manager.FontProperties 方法调整字体属性\n",
    "#该链接是FontProperties方法的介绍 https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties\n",
    "x1 = np.linspace(0.0, 5.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
    "\n",
    "font = FontProperties()\n",
    "font.set_family('serif')\n",
    "font.set_name('Times New Roman')\n",
    "font.set_style('italic')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "fig.subplots_adjust(bottom=0.15, left=0.2)\n",
    "ax.plot(x1, y1)\n",
    "ax.set_xlabel('time [s]', fontsize='large', fontweight='bold')\n",
    "ax.set_ylabel('Damped oscillation [V]', fontproperties=font)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### 6.字体的属性设置\n",
    " 字体设置一般有全局字体设置和自定义局部字体设置两种方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DejaVu Serif\n",
      "\n",
      "cmr10\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "STIXSizeFourSym\n",
      "\n",
      "STIXSizeTwoSym\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "STIXSizeFourSym\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "cmsy10\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "DejaVu Serif Display\n",
      "\n",
      "STIXGeneral\n",
      "\n",
      "DejaVu Serif\n",
      "\n",
      "DejaVu Sans Display\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "cmmi10\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXNonUnicode\n",
      "\n",
      "STIXSizeFiveSym\n",
      "\n",
      "cmb10\n",
      "\n",
      "STIXSizeTwoSym\n",
      "\n",
      "cmss10\n",
      "\n",
      "DejaVu Sans\n",
      "\n",
      "STIXSizeThreeSym\n",
      "\n",
      "STIXSizeOneSym\n",
      "\n",
      "cmtt10\n",
      "\n",
      "DejaVu Sans Mono\n",
      "\n",
      "cmex10\n",
      "\n",
      "STIXSizeThreeSym\n",
      "\n",
      "STIXSizeOneSym\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Constantia\n",
      "\n",
      "Arial\n",
      "\n",
      "Tahoma\n",
      "\n",
      "Calibri\n",
      "\n",
      "Georgia\n",
      "\n",
      "Consolas\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Candara\n",
      "\n",
      "FangSong\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Georgia\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Mongolian Baiti\n",
      "\n",
      "Javanese Text\n",
      "\n",
      "Lucida Sans Unicode\n",
      "\n",
      "Myanmar Text\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Calibri\n",
      "\n",
      "Constantia\n",
      "\n",
      "Segoe UI Emoji\n",
      "\n",
      "Candara\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Segoe UI Historic\n",
      "\n",
      "Consolas\n",
      "\n",
      "Mongolian Baiti\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Symbol\n",
      "\n",
      "Segoe Print\n",
      "\n",
      "Calibri\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Ink Free\n",
      "\n",
      "Courier New\n",
      "\n",
      "Segoe Script\n",
      "\n",
      "Microsoft PhagsPa\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Segoe UI Emoji\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Verdana\n",
      "\n",
      "Bahnschrift\n",
      "\n",
      "DengXian\n",
      "\n",
      "Verdana\n",
      "\n",
      "Microsoft Tai Le\n",
      "\n",
      "Consolas\n",
      "\n",
      "Georgia\n",
      "\n",
      "Ebrima\n",
      "\n",
      "Gadugi\n",
      "\n",
      "Consolas\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "KaiTi\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Microsoft New Tai Lue\n",
      "\n",
      "Verdana\n",
      "\n",
      "Corbel\n",
      "\n",
      "Candara\n",
      "\n",
      "Impact\n",
      "\n",
      "Constantia\n",
      "\n",
      "Gadugi\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Segoe Script\n",
      "\n",
      "Bahnschrift\n",
      "\n",
      "Wingdings\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Microsoft Yi Baiti\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Microsoft Sans Serif\n",
      "\n",
      "Arial\n",
      "\n",
      "Georgia\n",
      "\n",
      "Gadugi\n",
      "\n",
      "MT Extra\n",
      "\n",
      "SimHei\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Microsoft PhagsPa\n",
      "\n",
      "Segoe Script\n",
      "\n",
      "Corbel\n",
      "\n",
      "Corbel\n",
      "\n",
      "Sylfaen\n",
      "\n",
      "Segoe Script\n",
      "\n",
      "Microsoft Himalaya\n",
      "\n",
      "SimSun\n",
      "\n",
      "Constantia\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Arial\n",
      "\n",
      "Segoe UI Historic\n",
      "\n",
      "Segoe Print\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "MingLiU-ExtB\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Symbol\n",
      "\n",
      "Calibri\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Myanmar Text\n",
      "\n",
      "HoloLens MDL2 Assets\n",
      "\n",
      "Constantia\n",
      "\n",
      "Courier New\n",
      "\n",
      "Consolas\n",
      "\n",
      "Arial\n",
      "\n",
      "Microsoft PhagsPa\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Impact\n",
      "\n",
      "Consolas\n",
      "\n",
      "Microsoft New Tai Lue\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Courier New\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Webdings\n",
      "\n",
      "Segoe UI Symbol\n",
      "\n",
      "Consolas\n",
      "\n",
      "Lucida Sans Unicode\n",
      "\n",
      "Candara\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "DengXian\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Verdana\n",
      "\n",
      "Franklin Gothic Medium\n",
      "\n",
      "Gabriola\n",
      "\n",
      "Lucida Console\n",
      "\n",
      "Constantia\n",
      "\n",
      "Myanmar Text\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "HoloLens MDL2 Assets\n",
      "\n",
      "KaiTi\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Calibri\n",
      "\n",
      "SimSun-ExtB\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Tahoma\n",
      "\n",
      "Courier New\n",
      "\n",
      "Corbel\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Corbel\n",
      "\n",
      "Gadugi\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Marlett\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Calibri\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Arial\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "MS Gothic\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "FangSong\n",
      "\n",
      "Wingdings\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Myanmar Text\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Arial\n",
      "\n",
      "Corbel\n",
      "\n",
      "DengXian\n",
      "\n",
      "Ebrima\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Sylfaen\n",
      "\n",
      "Candara\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Webdings\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Microsoft Yi Baiti\n",
      "\n",
      "Microsoft Sans Serif\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Corbel\n",
      "\n",
      "Constantia\n",
      "\n",
      "MV Boli\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Cambria\n",
      "\n",
      "Courier New\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Franklin Gothic Medium\n",
      "\n",
      "Franklin Gothic Medium\n",
      "\n",
      "Arial\n",
      "\n",
      "Georgia\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Calibri\n",
      "\n",
      "Lucida Console\n",
      "\n",
      "Gabriola\n",
      "\n",
      "Courier New\n",
      "\n",
      "Cambria\n",
      "\n",
      "Microsoft Tai Le\n",
      "\n",
      "Arial\n",
      "\n",
      "Corbel\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Arial\n",
      "\n",
      "Franklin Gothic Medium\n",
      "\n",
      "Georgia\n",
      "\n",
      "Arial\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Cambria\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "DengXian\n",
      "\n",
      "Candara\n",
      "\n",
      "Ink Free\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Calibri\n",
      "\n",
      "Tahoma\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Verdana\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Corbel\n",
      "\n",
      "Courier New\n",
      "\n",
      "Microsoft PhagsPa\n",
      "\n",
      "SimHei\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Candara\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Constantia\n",
      "\n",
      "Segoe MDL2 Assets\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Times New Roman\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Cambria\n",
      "\n",
      "Candara\n",
      "\n",
      "Microsoft New Tai Lue\n",
      "\n",
      "DengXian\n",
      "\n",
      "Georgia\n",
      "\n",
      "Cambria\n",
      "\n",
      "Microsoft Tai Le\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Corbel\n",
      "\n",
      "Microsoft Tai Le\n",
      "\n",
      "Calibri\n",
      "\n",
      "Corbel\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Microsoft New Tai Lue\n",
      "\n",
      "Cambria\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Calibri\n",
      "\n",
      "SimSun-ExtB\n",
      "\n",
      "Segoe MDL2 Assets\n",
      "\n",
      "Verdana\n",
      "\n",
      "Georgia\n",
      "\n",
      "Microsoft Himalaya\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "Ebrima\n",
      "\n",
      "Javanese Text\n",
      "\n",
      "Calibri\n",
      "\n",
      "Consolas\n",
      "\n",
      "Verdana\n",
      "\n",
      "Segoe Print\n",
      "\n",
      "Leelawadee UI\n",
      "\n",
      "Segoe Print\n",
      "\n",
      "Calibri\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Nirmala UI\n",
      "\n",
      "Comic Sans MS\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Microsoft JhengHei\n",
      "\n",
      "Segoe UI\n",
      "\n",
      "Corbel\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Courier New\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Cambria\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Candara\n",
      "\n",
      "Verdana\n",
      "\n",
      "Cambria\n",
      "\n",
      "Trebuchet MS\n",
      "\n",
      "Segoe UI Symbol\n",
      "\n",
      "Malgun Gothic\n",
      "\n",
      "SimSun\n",
      "\n",
      "MV Boli\n",
      "\n",
      "MS Gothic\n",
      "\n",
      "Candara\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "Tahoma\n",
      "\n",
      "Palatino Linotype\n",
      "\n",
      "DengXian\n",
      "\n",
      "MingLiU-ExtB\n",
      "\n",
      "Microsoft YaHei\n",
      "\n",
      "Yu Gothic\n",
      "\n",
      "Ebrima\n",
      "\n",
      "Candara\n",
      "\n",
      "Sitka Small\n",
      "\n",
      "Candara\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#首先可以查看matplotlib所有可用的字体\n",
    "from matplotlib import font_manager\n",
    "font_family = font_manager.fontManager.ttflist\n",
    "font_name_list = [i.name for i in font_family]\n",
    "for font in font_name_list:\n",
    "    print(f'{font}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  [为方便在图中加入合适的字体，可以尝试了解中文字体的英文名称,该链接告诉了常用中文的英文名称](https://www.cnblogs.com/chendc/p/9298832.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#该block讲述如何在matplotlib里面，修改字体默认属性，完成全局字体的更改。\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']    # 指定默认字体为新宋体。\n",
    "plt.rcParams['axes.unicode_minus'] = False      # 解决保存图像时 负号'-' 显示为方块和报错的问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2092df4f700>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#局部字体的修改方法1\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fontmg\n",
    "\n",
    "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "plt.plot(x, label='小示例图标签')\n",
    "\n",
    "# 直接用字体的名字。\n",
    "plt.xlabel('x 轴名称参数', fontproperties='Microsoft YaHei', fontsize=16)         # 设置x轴名称，采用微软雅黑字体\n",
    "plt.ylabel('y 轴名称参数', fontproperties='Microsoft YaHei', fontsize=14)         # 设置Y轴名称\n",
    "plt.title('坐标系的标题',  fontproperties='Microsoft YaHei', fontsize=20)         # 设置坐标系标题的字体\n",
    "plt.legend(loc='lower right', prop={\"family\": 'Microsoft YaHei'}, fontsize=10)    # 小示例图的字体设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2092e8da910>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#局部字体的修改方法2\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fontmg\n",
    "\n",
    "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "plt.plot(x, label='小示例图标签')\n",
    "#fname为你系统中的字体库路径\n",
    "my_font1 = fontmg.FontProperties(fname=r'C:\\Windows\\Fonts\\simhei.ttf')      # 读取系统中的 黑体 字体。\n",
    "my_font2 = fontmg.FontProperties(fname=r'C:\\Windows\\Fonts\\simkai.ttf')      # 读取系统中的 楷体 字体。\n",
    "# fontproperties 设置中文显示，fontsize 设置字体大小\n",
    "plt.xlabel('x 轴名称参数', fontproperties=my_font1, fontsize=16)       # 设置x轴名称\n",
    "plt.ylabel('y 轴名称参数', fontproperties=my_font1, fontsize=14)       # 设置Y轴名称\n",
    "plt.title('坐标系的标题',  fontproperties=my_font2, fontsize=20)       # 标题的字体设置\n",
    "plt.legend(loc='lower right', prop=my_font1, fontsize=10)              # 小示例图的字体设置\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.数学表达式\n",
    "在文本标签中使用数学表达式。有关MathText的概述，请参见 [写数学表达式](https://matplotlib.org/tutorials/text/mathtext.html#sphx-glr-tutorials-text-mathtext-py),但由于数学表达式的练习想必我们都在markdown语法和latex语法中多少有接触，故在此不继续展开，愿意深入学习的可以参看官方文档.下面是一个官方案例，供参考了解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = np.sin(2*np.pi*t)\n",
    "\n",
    "plt.plot(t, s)\n",
    "plt.title(r'$\\alpha_i > \\beta_i$', fontsize=20)\n",
    "plt.text(1, -0.6, r'$\\sum_{i=0}^\\infty x_i$', fontsize=20)\n",
    "plt.text(0.6, 0.6, r'$\\mathcal{A}\\mathrm{sin}(2 \\omega t)$',\n",
    "         fontsize=20)\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('volts (mV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这是对前七节学习内容的总结案例\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "\n",
    "# 分别在figure和subplot上设置title\n",
    "fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')\n",
    "ax.set_title('axes title')\n",
    "\n",
    "ax.set_xlabel('xlabel')\n",
    "ax.set_ylabel('ylabel')\n",
    "\n",
    "# 设置x-axis和y-axis的范围都是[0, 10]\n",
    "ax.axis([0, 10, 0, 10])\n",
    "\n",
    "ax.text(3, 8, 'boxed italics text in data coords', style='italic',\n",
    "        bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})\n",
    "\n",
    "ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)\n",
    "font1 = {'family': 'Times New Roman',\n",
    "        'color':  'purple',\n",
    "        'weight': 'normal',\n",
    "        'size': 10,\n",
    "        }\n",
    "ax.text(3, 2, 'unicode: Institut für Festkörperphysik',fontdict=font1)\n",
    "ax.text(0.95, 0.01, 'colored text in axes coords',\n",
    "        verticalalignment='bottom', horizontalalignment='right',\n",
    "        transform=ax.transAxes,\n",
    "        color='green', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、Tick上的文本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设置tick（刻度）和ticklabel（刻度标签）也是可视化中经常需要操作的步骤，matplotlib既提供了自动生成刻度和刻度标签的模式（默认状态），同时也提供了许多让使用者灵活设置的方式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.简单模式\n",
    "可以使用axis的`set_ticks`方法手动设置标签位置，使用axis的`set_ticklabels`方法手动设置标签格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "x1 = np.linspace(0.0, 5.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用axis的set_ticklabels方法手动设置标签格式的例子\n",
    "fig, axs = plt.subplots(2, 1, figsize=(5, 3), tight_layout=True)\n",
    "axs[0].plot(x1, y1)\n",
    "axs[1].plot(x1, y1)\n",
    "ticks = np.arange(0., 8.1, 2.)\n",
    "tickla = [f'{tick:1.2f}' for tick in ticks]\n",
    "axs[1].xaxis.set_ticks(ticks)\n",
    "axs[1].xaxis.set_ticklabels(tickla)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<a list of 14 Line2D ticklines objects>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#一般绘图时会自动创建刻度，而如果通过上面的例子使用set_ticks创建刻度可能会导致tick的范围与所绘制图形的范围不一致的问题。\n",
    "#所以在下面的案例中，axs[1]中set_xtick的设置要与数据范围所对应，然后再通过set_xticklabels设置刻度所对应的标签\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(2, 1, figsize=(6, 4), tight_layout=True)\n",
    "x1 = np.linspace(0.0, 6.0, 100)\n",
    "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n",
    "axs[0].plot(x1, y1)\n",
    "axs[0].set_xticks([0,1,2,3,4,5,6])\n",
    "\n",
    "axs[1].plot(x1, y1)\n",
    "axs[1].set_xticks([0,1,2,3,4,5,6])#要将x轴的刻度放在数据范围中的哪些位置\n",
    "axs[1].set_xticklabels(['zero','one', 'two', 'three', 'four', 'five','six'],#设置刻度对应的标签\n",
    "                   rotation=30, fontsize='small')#rotation选项设定x刻度标签倾斜30度。\n",
    "axs[1].xaxis.set_ticks_position('bottom')#set_ticks_position()方法是用来设置刻度所在的位置，常用的参数有bottom、top、both、none\n",
    "print(axs[1].xaxis.get_ticklines())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Tick Locators and Formatters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除了上述的简单模式，还可以使用`Tick Locators and Formatters`完成对于刻度位置和刻度标签的设置。\n",
    "其中[Axis.set_major_locator](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_major_locator.html#matplotlib.axis.Axis.set_major_locator)和[Axis.set_minor_locator](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_minor_locator.html#matplotlib.axis.Axis.set_minor_locator)方法用来设置标签的位置，[Axis.set_major_formatter](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_major_formatter.html#matplotlib.axis.Axis.set_major_formatter)和[Axis.set_minor_formatter](https://matplotlib.org/api/_as_gen/matplotlib.axis.Axis.set_minor_formatter.html#matplotlib.axis.Axis.set_minor_formatter)方法用来设置标签的格式。这种方式的好处是不用显式地列举出刻度值列表。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set_major_formatter和set_minor_formatter这两个formatter格式命令可以接收字符串格式（matplotlib.ticker.StrMethodFormatter）或函数参数（matplotlib.ticker.FuncFormatter）来设置刻度值的格式 。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Tick Formatters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收字符串格式的例子\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True)\n",
    "for n, ax in enumerate(axs.flat):\n",
    "    ax.plot(x1*10., y1)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('%1.1f')\n",
    "axs[0, 1].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('-%1.1f')\n",
    "axs[1, 0].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('%1.5f')\n",
    "axs[1, 1].xaxis.set_major_formatter(formatter)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收函数的例子\n",
    "def formatoddticks(x, pos):\n",
    "    \"\"\"Format odd tick positions.\"\"\"\n",
    "    if x % 2:\n",
    "        return f'{x:1.2f}'\n",
    "    else:\n",
    "        return ''\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)\n",
    "ax.plot(x1, y1)\n",
    "ax.xaxis.set_major_formatter(formatoddticks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Tick Locators "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "在普通的绘图中，我们可以直接通过上图的set_ticks进行设置刻度的位置，缺点是需要自己指定或者接受matplotlib默认给定的刻度。当需要更改刻度的位置时，matplotlib给了常用的几种locator的类型。如果要绘制更复杂的图，可以先设置locator的类型，然后通过axs.xaxis.set_major_locator(locator)绘制即可  \n",
    "locator=plt.MaxNLocator(nbins=7)  \n",
    "locator=plt.FixedLocator(locs=[0,0.5,1.5,2.5,3.5,4.5,5.5,6])#直接指定刻度所在的位置  \n",
    "locator=plt.AutoLocator()#自动分配刻度值的位置  \n",
    "locator=plt.IndexLocator(offset=0.5, base=1)#面元间距是1，从0.5开始  \n",
    "locator=plt.MultipleLocator(1.5)#将刻度的标签设置为1.5的倍数  \n",
    "locator=plt.LinearLocator(numticks=5)#线性划分5等分，4个刻度  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 接收各种locator的例子\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True)\n",
    "for n, ax in enumerate(axs.flat):\n",
    "    ax.plot(x1*10., y1)\n",
    "\n",
    "locator = matplotlib.ticker.AutoLocator()\n",
    "axs[0, 0].xaxis.set_major_locator(locator)\n",
    "\n",
    "locator = matplotlib.ticker.MaxNLocator(nbins=10)\n",
    "axs[0, 1].xaxis.set_major_locator(locator)\n",
    "\n",
    "\n",
    "locator = matplotlib.ticker.MultipleLocator(5)\n",
    "axs[1, 0].xaxis.set_major_locator(locator)\n",
    "\n",
    "\n",
    "locator = matplotlib.ticker.FixedLocator([0,7,14,21,28])\n",
    "axs[1, 1].xaxis.set_major_locator(locator)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 此外`matplotlib.dates` 模块还提供了特殊的设置日期型刻度格式和位置的方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "import datetime\n",
    "# 特殊的日期型locator和formatter\n",
    "locator = mdates.DayLocator(bymonthday=[1,15,25])\n",
    "formatter = mdates.DateFormatter('%b %d')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True)\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "base = datetime.datetime(2017, 1, 1, 0, 0, 1)\n",
    "time = [base + datetime.timedelta(days=x) for x in range(len(x1))]\n",
    "ax.plot(time, y1)\n",
    "ax.tick_params(axis='x', rotation=70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**其他进阶案例**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这个案例中展示了如何进行坐标轴的移动，如何更改刻度值的样式\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.linspace(-3,3,50)\n",
    "y1 = 2*x+1\n",
    "y2 = x**2\n",
    "plt.figure()\n",
    "plt.plot(x,y2)\n",
    "plt.plot(x,y1,color='red',linewidth=1.0,linestyle = '--')\n",
    "plt.xlim((-3,5))\n",
    "plt.ylim((-3,5))\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "new_ticks1 = np.linspace(-3,5,5)\n",
    "plt.xticks(new_ticks1)\n",
    "plt.yticks([-2,0,2,5],[r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four'])\n",
    "'''\n",
    "上一行代码是将y轴上的小标改成文字,其中，空格需要增加\\，即'\\ ',$可将格式更改成数字模式，如果需要输入数学形式的α，则需要用\\转换，即\\alpha\n",
    "如果使用面向对象的命令进行画图，那么下面两行代码可以实现与 plt.yticks([-2,0,2,5],[r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four']) 同样的功能\n",
    "axs.set_yticks([-2,0,2,5])\n",
    "axs.set_yticklabels([r'$one\\ shu$',r'$\\alpha$',r'$three$',r'four'])\n",
    "'''\n",
    "ax = plt.gca()#gca = 'get current axes' 获取现在的轴\n",
    "'''\n",
    "ax = plt.gca()是获取当前的axes，其中gca代表的是get current axes。\n",
    "fig=plt.gcf是获取当前的figure，其中gcf代表的是get current figure。\n",
    "\n",
    "许多函数都是对当前的Figure或Axes对象进行处理，\n",
    "例如plt.plot()实际上会通过plt.gca()获得当前的Axes对象ax，然后再调用ax.plot()方法实现真正的绘图。\n",
    "\n",
    "而在本例中则可以通过ax.spines方法获得当前顶部和右边的轴并将其颜色设置为不可见\n",
    "然后将左边轴和底部的轴所在的位置重新设置\n",
    "最后再通过set_ticks_position方法设置ticks在x轴或y轴的位置，本示例中因所设置的bottom和left是ticks在x轴或y轴的默认值，所以这两行的代码也可以不写\n",
    "'''\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "ax.spines['bottom'].set_position(('data',0))#axes 百分比\n",
    "ax.xaxis.set_ticks_position('bottom')   #设置ticks在x轴的位置\n",
    "ax.yaxis.set_ticks_position('left')     #设置ticks在y轴的位置\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、[legend](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend)（图例）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图例的设置会使用一些常见术语，为了清楚起见，这些术语在此处进行说明：\n",
    "##### legend entry（图例条目）\n",
    "图例有一个或多个legend entries组成。一个entry由一个key和一个label组成。\n",
    "##### legend key（图例键）\n",
    "每个 legend label左面的colored/patterned marker（彩色/图案标记）\n",
    "##### legend label（图例标签）\n",
    "描述由key来表示的handle的文本\n",
    "##### legend handle（图例句柄）\n",
    "用于在图例中生成适当图例条目的原始对象\n",
    "\n",
    "以下面这个图为例，右侧的方框中的共有两个legend entry；两个legend key，分别是一个蓝色和一个黄色的legend key；两个legend label，一个名为‘Line up’和一个名为‘Line Down’的legend label"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "常用的几个参数：\n",
    "\n",
    "(1)设置图列位置\n",
    "\n",
    "plt.legend(loc='upper center') 等同于plt.legend(loc=9)\n",
    "\n",
    "\n",
    "\n",
    "| |  |  |\n",
    "| ------------------------------------------------------- | ------------------------------------------ | ------------------------------------------------------------ |\n",
    "|  0: ‘best'  \n",
    "1: ‘upper right'  \n",
    "2: ‘upper left'  \n",
    "3: ‘lower left'                                                        |   \n",
    "4: ‘lower right'  \n",
    "5: ‘right'  \n",
    "6: ‘center left'                                           |    \n",
    "7: ‘center right'  \n",
    "8: ‘lower center'  \n",
    "9: ‘upper center'  \n",
    "10: ‘center'                                                           |  \n",
    "\n",
    "\n",
    "(2)设置图例字体大小\n",
    "\n",
    "fontsize : int or float or {‘xx-small’, ‘x-small’, ‘small’, ‘medium’, ‘large’, ‘x-large’, ‘xx-large’}\n",
    "\n",
    "(3)设置图例边框及背景\n",
    "\n",
    "plt.legend(loc='best',frameon=False) #去掉图例边框  \n",
    "plt.legend(loc='best',edgecolor='blue') #设置图例边框颜色  \n",
    "plt.legend(loc='best',facecolor='blue') #设置图例背景颜色,若无边框,参数无效\n",
    "\n",
    "(4)设置图例标题\n",
    "\n",
    "legend = plt.legend([\"CH\", \"US\"], title='China VS Us')\n",
    "\n",
    "(5)设置图例名字及对应关系\n",
    "\n",
    "legend = plt.legend([p1, p2], [\"CH\", \"US\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2092fca3100>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z/locU3rXYlqf2lLc7USazqJRSrkDNbXWsx7yeE7g38J/GnAHLgP//kYln+W+sBel/ODlUNgUAJu+NYaXtfkaqneVcQciTXadiOG9hREcib5Jl1olGdXOi4IyP8aupNrBK6WyA2211guUUtmUUr5KqTL37dYa6GG5XRo4CGwE6li2+WIc6hH2JGsOeOp9o9AXKANBA+G3XnD9rNnJhB27eSeRT5dG0W3aVuISkpnVvw4Bz9WQ4m6H0vJL1peAVkqpucAGoAAw6b591gK3lVIdAVet9W6t9UHgglKqHxBjuS/sUbFq8NI6aPk5HNkIgfVg1yzQ2uxkws5sOhRNq/GhzNpynL71y7DmTX+aVS5qdizxEErbyV9iPz8/vXPnTrNjiMtHYNlwOL4JyjYxzr4pVN7sVMJk124l8PmKvSzYdZrybnn4qqsPdco+8Gs1YQKl1C6ttV9Kj8mFTuJehSsYp1O2Hw/nwmFKQ9gyWYaXZWKr95yj+bgQFv19hqHNKrByWBMp7g5CRhWIB7m4gF9/qNgSVrwFaz/8/+FlxbzMTicyyMXYOD5ZEsWqPefxKu7KTy/WoXrJ/GbHEukgHbx4uPwl4fnfoOsPcOU4TPeHjV/K8DInp7Vm4a7TtAgIZf3+i7zbqjJLXmskxd0BSQcvHk0p8O4G5ZvB6pEQMtYYXtYpEErVNjudsLLTV27xweI9hB6Mxq9MQcZ29cGzaF6zY4nHJB28SJs8RaDr9/D8fIi7Bj80hzUfyvAyJ5GcrJm95Tgtx4Wy83gMn3Wsxu8vN5Di7uCkgxfpU7k1lGkAf35irAn77/Cycv5mJxOP6fDFG4wMimDniSv4V3JjTOfqlCoo82OcgXTwIv1y5ocO442lAlEwuwMsHWZ09sJhJCQlE7jxMG0nbOLQxRt8292X2f3rSHF3ItLBi8dXrokxvCx4DGwNhENrjeFllduYnUykYs+Za7y3MIK9567T1tudTztWo2g+mR/jbKSDF08me27jCtiX1kGuQvBrT1g4AG5Em51MpCAuIYmvVu+nU2AYF2PvMK1PLab0ri3F3UlJBy+so2RtGBwMYeONdWCPbIQ2X4F3dxleZif+Oh7DiIURHL10k+61S/FROy/y585mdixhQ9LBC+vJmh2avgevbDLGGywaBPN6wLXTZifL1G7cSeTjJXvoPm0r8UnJ/DywLt9095XinglIgRfWV7QqDFwLrb40ZtoE1oe/foDkB1Z6FDYWfOAircaF8vO2E7zYsCxr3vCnSUVZeyGzkEM0wjZcskCDocYvXJcNN0Ye7FlkDC8rXMHsdE7vys14Rq/Yy6LdZ6jgloeFrzSgdhmZH5PZSAcvbKtQOei7xDhX/nwkTG0IYRMgKdHsZE5Ja83KyHO0GBfC0n/O8tpTnqwY1kSKeyYlHbywPaWgVl/wbAEr3oY/Pza6+U6Twd3b7HRO4+L1OEYt2cOaqAtUL+nK7AF1qVZC5sdkZtLBi4zjWhx6/gLdfjJ+8TqjGWz4HBLvmJ3MoWmt+f2vUzQPCCH4QDQj21Thj6GNpLiL1Dt4y3qrvYBooB7wsdY6OS37KKXCgGOW3T7WWh+1ZnjhgJSC6l0sw8veh9BvYO9So5svXdfsdA7nVMwt3l8UyebDl6hbthBju3pT3k3mxwhDWjr41kCS1noZcA6okY59pmqt+1j+SHEX/y93IegyHXovhPib8ENLWDXSuC1SlZSs+XHzMVqOC+Xvk1cY/Wx1fhtcX4q7uEdajsEHA4Utt4sDx9OxT32lVEGgEjA8hc5/MDAYwMPDI+2phfOo2AKGboX1n8H2qXBgBXSYCBWeMjuZ3Tp0IZYRQRHsPnmVZpXd+KKzNyUL5DI7lrBDaV6TVSnlCTTWWs9K6z5Kqapa631Kqf7AMa118MOeK2uyCo6HwdLXIeYI1OxjjEDIVdDsVHYjISmZacFHmLThMLlzZOGTDl48W6MkSq4UztQetSZrms6iUUq5AzVTKe737GM5Lh9jefg04J6OzCIzKtsIhoRB8FjYMgkO/QntvoOqHcxOZrrI09d4d2E4+8/H0s6nOJ91rEaRvDnMjiXsXKrH4JVS2YG2WusFSqlsSilfpVSZ1PbBOC7fw7JLaeCgtcMLJ5QtF7T4DAathzxFYX4f+L0f3LhodjJTxCUk8eWqfXQK3EzMzXimv1CbwF61pLiLNEnLL1lfAloppeYCG4ACwKRU9kkE1gK3lVIdAVet9W6rpRbOr0RNGLwRnh4FB1bC5Drwz6+QxkOKzmDb0cu0Hh/K9JCjPOdXmj/fakqravIfYZF2aT4Gb2tyDF48VPQBWPIanN4Bns2h/XgoUNrsVDYTG5fA2FX7+WX7SUoXysXYLj408ixidixhpx51DF4udBL2z60yDFgNbb6GE1thSn3YMdMph5dt3H+RluNCmbfjJAMbl2PNG/5S3MVjkwIvHINLFqj3snFKZak6sPIdmNUWLh0yO5lVxNyM543f/qb/rL/ImyMrQUMaMqq9F7mzyzQRa4iNjWXixIlmx8hwUuCFYylYBl5YDJ2mwMW9MLURbAqApASzkz0WrTXLws/SIiCE5RHnGPZMRZYPa0wtDzk91Jp27tzJvn37HtjeokUL7OUwtS1IgReORymo2Rte/QsqtTQukpr5NJwLNztZupy/FsegObt4/de/KVkwF8uHNeatFpXIkTWL2dGczlNPPUWxYsXu2aa15pdffnHq6wikwAvHla8Y9JgLz82B2PMw4ylY/z9IiDM72SNprfl1x0laBISw6VA0H7atyqIhDani7mp2tEwlNDSUUaNG/Xd/yZIlDBkyhPnz5/PRRx/919nPnDmTpUuX8t1335kV9bFJgReOz6sTvLodfHrApu9gWmM4uc3sVCk6cfkmvWZu5/1FkXiVcGXNG/4M8i9P1izyVzGjNW3alOLFi/9339fXlypVqtCjRw88PDw4d+4c4eHhZM2alY4dO1K0aFG2b99uYuL0k0+VcA65C0HnqdAnCBLj4MfWsPI9uHPD7GSAMRzs+01HaTU+lMgz1xjT2ZtfB9WnbJE8ZkcTd8mf3xixnD17duLj49m/fz8xMTEEBweTNWtWhzucI7+iF87Fs7lleNn/YMcMOLAKOowztpvkwPlY3guKIPzUVZ6uUpQvOleneH4ZDuYIKlSowO3bt2nWrBnx8fHExsaaHSldpIMXzidHPmj7jXHufNYcMLcrLB4Ct2JSf64VxScmM37dQdpP2sSpmFtM6FmDH/r5SXE3QXBwMBEREQQHB/+3LSwsjIiICMLCwgDYu3cvUVFRAERFRbF37178/Py4cuUK8+bNY/78+f91+I5CrmQVzi0hDkK/hs3jIXdhaPetcczexv45dZURCyM4cCGWTjVK8HF7LwrL/BhhA3Ilq8i8suWEZz6GwcGQzx1+72sMMIs9b5O3ux2fxBcr9tJlShjXbifwfV8/JvSsKcVdmEKOwYvMobgPDNpgjCEOHgvH6kKrL6FGL+O8eivYcuQSI4MiORlzi171PBjZpgquObNZ5bWFeBzSwYvMI0s2aPKWMXO+qBcsGQo/d4YrJ57oZa/HJfD+okh6zdyOUjBvUD3GdPaW4i5MJwVeZD5FKsKLK6Htt3D6L5jSALZNg+SkdL/Uur0XaBEQwvy/TjLYvzyrh/vTsIIMBxP2QQ7RiMzJxQXqDoJKrWD5m7B6BEQtgo6TjOmVqbh84w6fLtvLsvCzVHHPx4wX/PAtXcD2uYVIB+ngReZWwAN6L4TO0+HSQeMq2NBvHjq8TGvNkn/O0DwghNV7zvFm80osfa2xFHdhl6TAC6EU+PaEV3dA5baw4XNjrs3Zf+7Z7ezV2wycvZPhv/1DmcJ5WDGsCcObVyR7Vvlr5AiWLl3Kc889R1zcvbOKrDVRMjAwkHfeeQeA8PBwAgMDn/g1n1Sqh2gsi2f3AqKBesDHWusHVlpQSg0HrgL5tdYTH7ZNCLuVtyg8Nxv2LYMVbxsTKhu+TrL/CH79J5ovV+4nMTmZj9pVpX+jcmRxcazL1jM7Hx8fvLy8yJkz53/brDlRslq1auTJY4ye8PX1tYurXtNyDL41kKS1XqaU8gBqAPesr6qU8gSKa60nKKU+UUpVwViX9Z5tWuv91v4ChLC6qh2gbGNY+xGEjef8tgX8cWsgPuUbMbaLDx6Fc5ud0OF9tiyKvWevW/U1vUq48kmHaul6TmhoKPPmzWP69OmAMVFy9erVNGvWjMjISEaPHo1SipkzZ1KsWDEOHTrE22+/nebX//XXX9myZQuNGzf+77m5cmXclcxp+b9lMLDZcrs4cDyFfZ4GdlhuhwNNH7LtHkqpwUqpnUqpndHR0WlPLYSNJWbPz/QCb/Fi0ockJ8azIMf/+KX4AjzyJJodTViRrSdKNmjQAC8vL3r06EGzZs0ICgqyxZfxUKl28Frrq8BVS5d+WGud0kCPIsBRy+0bQFVApbDt/teeAcwAY1RBesMLYQv7zl1nRFAEEaev0bzq02RrOxB2fYvaNhUOroYO46FiC7NjOrT0dtoZydoTJf/t2IsWLfrf3JuMkqbTJJVS7kBNrfWsh+xyGchnuZ3Pcl+lsE0Iu3UnMYnADYeZEnyE/LmyMblXTdp5Fzf+Qrf+Eqp1hiWvwS/dwKensS13IbNji8cUFRVFtWqp/0OT1omSefLk4c6dOwBcu3aNfPmM8peUZFxfcfz4cTw9Pa2UPm1SPUSjlMoOtNVaL1BKZVNK+Sqlyty320agjuW2L8ZhnZS2CWGXdp+8QvuJm5m44TAdfEuw7q2mtPcpcW+3VrouvLIJ/N+DPQthch3YswjsZGCfeLSIiAgOHDhAcHAwEydOZOfOnVadKFmzZk3Onz/P/PnzWbNmDT4+PoAxtXLJkiVs376dTp1sP+jubqlOk1RKDcU4fp4AlAE+At7WWne8b78nOotGpkkKM9yKT+TbNQf5acsx3F1zMqazN09VKZr6E89HGt38uX+gSnvjqljX4qk+TWQuy5cvZ/369Xz77bdkyWKbtXYfNU1SxgWLTGvzoUu8vziCUzG36VPfgxGtq5AvPfNjkhJhWyBsHANZckCrz6HmC1YbXiZEWsi4YCHucu12Au8tDKfPD9vJ6uLC/MH1+fxZ7/QVd4AsWaHRcBiyBdyrw9LXYU5HiDlmm+BCpJMUeJGprIk6T4uAEIJ2n+GVphVYNbwJ9coXfrIXLVwB+i2HdgFw5m+Y2hC2Tnms4WVCWJMMGxOZQnTsHT5dGsWKyHNULe7KD/3q4F3KisuvubhAnYH/P7xszfuwJwg6TYaiD5whLESGkA5eODWtNYt2n6bFuBD+3HuBd1pWYulrjaxb3O+WvxT0+h26zISYozCtCYR8DYnxtnk/IR5BOnjhtM5cvc2HiyMJPhBNLY8CfN3NB8+i+VJ/4pNSCnyeg/JPGWOIN34BUX9Ap0lQsrbt318IC+nghdNJTtb8vPU4LQNC2H40hk86eLHglYYZU9zvltcNuv0IPX+F2zHwfXNjvk38rYzNIQDbT5NcunQpXbp0YdWqVUycOJHk5AdmMmY4KfDCqRyJvkGPGVsZtSSKWmUKsvZNf/MnP1ZpC69uN06h3DIJpjWCY5vMy5NJ2XqapI+PDz4+PrRp04ZKlSoxd+7cJ37NJyWHaIRTSExKZsamo4xfd4icWV34ppsP3WqXsspfXKvImR86ToTqXWHZMJjdHmr3hxafGY9lNqtGGheLWZO7N7QZm66n2GqaZIMGDXj11Vfp27cvv/zyC8WKFePo0aN0796doKAgrly5wq5duxgxYgRz5szhxRdf5IsvvqBTp04cPXqUfv364eHh8VjfhrtJBy8cXtTZazw7JYyvVx/gqcpurHurKd39SttPcb9b+aYwZCs0eA12z4bA+nBwjdmpMi1bTZPMmzcvFy9e5MCBA8TGxtK8eXN69uzJuHHjeP7559Fa4+vri9aaNm3a4OvrS8mSJenduzc9evRg8+bNqb5HWkgHLxxWXEISkzYcYlrIUQrmzs7U3rVo4+0A4wKy54ZWX/z/8LJ5z4F3d2g9FvJkkgW709lpZyRrTJO8du0ahQsXJjw8nPLlywPg6urK2bNnyZMnD3FxcVSqVImNGzfi7++f4vtag3TwwiHtPB5Du4mbCNx4hGdrlGTdW/6OUdzvVsoPXg6FpiONs2wC60LkQhlelkH+HSqWmgoVKlC4cGGaNWtG165dqVChQqrPCQsLo02bNlSrVo1Tp04BcOvWLdzd3QFwcXHBw8ODdevW4evr+/hfRCqkgxcO5eadRL5Zc4DZW49TIn8uZg+oS9NKbmbHenxZs8NT74NXR6ObDxoIkQuMq2LzlzQ7nVO5e5pkREQE+fPn5+rVq/9Nk2zUqNED0ySLFi1K27Zt2bRpE/PmzSMpKYnnn3/+oa+/b98+Vq1axYkTJxg6dCguLi7s3r2btWvXcurUKd544w0ASpcuTcWKFWnevDnZs2fn9OnTREZGcuPGjf+mWFqDDBsTDiP0YDTvL4rk7LXb9K1fhndbVyFvDifqUZKTYNtUY9HvLNmgxf+gVj/jKlkhHkKGjQmHdvVWPO8sCKfvjzvIkc2F319uwGedqjtXcQdwyQINX4OhW6C4Lyx/wxhedvmI2cmEg5ICL+zaqshzNA8IZfHfZxjarAIrhzWhTlknX0WpUHnotww6TIBz4TC1kXH+vAwvE+nkZC2QcBYXY+P4ZEkUq/acx6u4K7P616F6yUx0vrhSUPtFqNgSlr9lXAG7ZxF0CoRiXmanEw5COnhhV7TWLNh5ihYBoazff5F3W1VmyWuNMldxv5trCXj+V+j6A1w9AdP9YeOXkHjH7GTCAaRlTdaGSqmFj3i8hlJqm1JqrlJqtVKqvWV7mGXbXKVUeWuGFs7pVMwt+v64g3cXRlCxaF5WDmvCq095ki1LJu9DlALvbvDqX8a58yFjYXpTOC0nJYhHS/UQjdZ6i1Jq8CN2yQb4a63jlVK9tNbLLdunaq3NH8Yg7F5ysmbO1uN8veYACvhfp2r0qVcGFzPnx9ijPIWh60yj2C9/0xheVn8oPP0hZM9jdjphh574GLzW+i8ApVRJ4MpdD9VXShUEKgHDtdYPjFaz/MMxGLDK3AXheA5fjGVEUCS7TlzBv5IbYzpXp1TB3GbHsm+VWsHQbbDuE2NN2AMroMNEYwyCEHex5v99XwDW3nU/UGs9CdgN+Kf0BK31DK21n9baz83NgS9WEemWkJRM4MbDtJ2wmcMXb/Bdd19m968jxT2tcrpC+3Hw4gpQLsbplEtfh9tXzU4m7IhVzqJRxnCGclrrJMv9nECM5eHTgLs13kc4hz1nrvHewgj2nrtOW293PutYHbd8OcyO5ZjKNoZXwiD4S9g6GQ79aVwFW6Wt2cmEHUhXB6+UyqaUKpPCQxW59x+L1kAPy+3SwMHHiyecSVxCEl+t3k+nwDCib9xhWp9aTOldW4r7k8qeG1qOhpfWQ65C8NvzsKA/3Ig2O5kwWVrOovEHmiilOgG1gEkp7JYTiL3r/lrgtlKqI+Cqtd5tjbDCce04FkPbCZuYGnyErrVKsu7NprSu7mDDwexdyVowOBie+gj2L4fAOhA+X4aXZWIyi0bY1I07iXy1aj8/bztBqYK5GNvFh8YVM8lIXDNd3A9LX4PTfxkXS7UfZywILpyOzKIRpth44CItA0KYu/0E/RuVZc0b/lLcM0rRKjBgjTFj/vhmY2GRv74HO1gnVGQcGVUgrO7KzXhGL9/Lor/P4Fk0LwtfaUjtMgXNjpX5uGSB+kOgUmtYNhxWvG2MO+g4CQqnPtNcOD7p4IXVaK1ZEXGOFuNCWBp+ltef9mTFsMZS3M1WqBz0XQIdJ8P5PTC1IWweD0mJZicTNiYdvLCKC9fjGPXHHtbuvYB3yfzMGVAPrxKuZscS/1IKar0Ans1h5TvGRVJRi6HTZGOxauGUpIMXT0Rrzfy/TtI8IISQg9G836YKi4c2lOJur1yLQ4+50H0WXD8DM5oZC4zI8DKnJB28eGwnL9/i/cURhB2+TN1yhRjbxZvybnnNjiVSo5QxtKxcU1jzAYR+A3uXGt186bpmpxNWJB28SLekZM0Pm4/Ranwo4aeu8fmz1fltUH0p7o4mdyHoPA16L4T4m/BDS1g1Eu7cMDuZsBLp4EW6HLoQy3tBEfx98irNKrsxprM3JQrkMjuWeBIVW8Cr22DdZ7B9qmV42QSo8LTZycQTkg5epEl8YjIT1x+i3cTNHL90k/E9avDTi3WkuDuLHPmg3bfQfxVkyQ4/d4Ylr8LtK6k/V9gt6eBFqiJOX+W9hRHsPx9LB98SfNLBiyJ5ZX6MUyrT0BheFjIWwiZahpd9B1U7mJ1MPAbp4MVD3Y5P4suV+3g2MIwrt+KZ2dePSc/XlOLu7LLlhOafwqANkLcozO8Dv/eF2AtmJxPpJB28SNG2o5cZGRTB8cu3eL5uaUa2qUr+XNnMjiUyUokaMGgjhE2AkK/gaIgx+sC3p3EmjrB70sGLe8TGJfDh4kh6zthGsoZ5L9Xjyy4+UtwzqyzZwP8d47CNW2X44xWY2xWunjQ7mUgDKfDiPxv2X6DluFB+3XGSlxqXY/UbTWjoKcPBBOBWCfqvhjbfwMltMKUB7Jgpw8vsnBR4QczNeN747W8GzNpJ3hxZCRrSkI/ae5E7uxzBE3dxcYF6g2HoVuOCqJXvwKy2cOmQ2cnEQ0iBz8S01iwNP0vzgBBWRJ5j+DMVWT6sMTU9ZDiYeISCZaDPInh2KlzcB1MbwaYASEowO5m4T6otmlKqIfCW1rrbI/YJA45Z7n6stT6qlBoOXAXya60nWiOssJ7z1+L46I9I1u27iG+p/HzVrR5V3GV+jEgjpaBGL6jwjNHJr//s/4eXFfc1O52wSLWD11pvAVK7dnmq1rqP5c9RpZQnUFxrPRsoqJSqYo2w4slprfl1x0laBISw+fAlPmxblUVDG0lxF48nXzHo8TM8Nwdiz8OMp4wrYhPizE4msN5pkvWVUgWBSsBw4Glgh+WxcKApsP/+JymlBgODATw8PKwURTzMics3GRkUydajl6lfvhBju/hQtkges2MJZ+DVCco2gbWjYHMA7FtmdPMe9c1OlqlZ6xh8oNZ6ErAb8AeKANctj90ACqX0JK31DK21n9baz83NzUpRxP2SkjXfbzpKq/Gh7DlzjTGdvZn3Un0p7sK6cheCZwON4/OJd+DH1rDyXbgTa3ayTOuJO3ilVE4gxnL3NOAOXAbyWbbls9wXJjhw3hgOFn7qKs9UKcrnnatTPL/MjxE25PmMcabNhtGwfTocWAUdxhuLjYgMla4OXimVTSlV5r7NrYEeltulgYPARqCOZZsvEPwEGcVjiE9MZtyfB2k/aROnYm4x8fmafN/PT4q7yBg58kKbr2DAasiWy7g4avErcCsm9ecKq0m1wCul/IEmSqlOQC1g0n27rAVuK6U6Aq5a691a64PABaVUPyDGcl9kkH9OXaX9pE1MWH+Itt7FWfdWUzr6lkDJ5eUio3nUh5c3QZN3IOJ3CKwLUX+YnSrTUFprszMA4Ofnp3fu3Gl2DId2Oz6J79Ye4MewYxTNl5MvOlfnmarFzI4lhOFcBCx9Dc6FG9Mp234L+dzNTuXwlFK7tNZ+KT0mlyo6iS1HLjEyKJKTMbfoVc+DkW2q4JpT5scIO1LcB17aAFsnwcYv4VhdaDUGavSW4WU2IleyOrjrcQm8vyiCXjO3oxT8Oqg+Yzp7S3EX9ilLVmj8JgzZAkWrGYuK/NwZrpwwO5lTkgLvwP7ce4EWASHM/+sUL/uXZ/VwfxpUKGx2LCFSV8QTXlxhHKY5/ZcxvGzbNEhOMjuZU5FDNA7o0o07fLo0iuUR56jino+Zff3wKVXA7FhCpI+LC9QdBJVaw/I3YPUI2BNkXCDlVtnsdE5BOngHorXmj7/P0CIghDVR53mrRSWWvtZYirtwbAVKQ++F0Hk6XD4E0xpD6DcyvMwKpIN3EGev3uajP/awYf9FapQuwNfdfKhULF/qTxTCEShlrBRV4WlY9R5s+Nw4nbLTZChR0+x0Dks6eDuXnKyZu+0ELceFsvXIZUa19yJoSEMp7sI55S0K3WdBj1/g5iWY+Qz8+Qkk3DY7mUOSDt6OHbt0k5FBEWw/FkMjz8J82dkHj8K5zY4lhO1VbQ9lG8PajyBsvDG8rOMkKNvI7GQORTp4O5SYlMz0kCO0Hh/K3nPX+aqrN3MH1pPiLjKXXAWMQzR9l0ByorF61PK3IO56qk8VBung7czes9cZERRB5JlrtPAqxufPVqeYa06zYwlhnvLNLMPLPodtU+HgGmg/Diq1NDuZ3ZMO3k7cSTTGDHScvJlz124T2KsWM16oLcVdCIDseaD1lzDwT2OQ2bzusGgw3JRBtY8iHbwd2HXiCiOCIjh88QZdapZkVHsvCubJbnYsIexP6Trwcihs+s74c3g9tP0GqnWWcQcpkA7eRLfiE/lsWRTdpm3h1p1Efupfh4AeNaS4C/EoWXPAUx/A4BDjHPqF/eG33nD9nNnJ7I4UeJNsPnSJluNC+SnsOH3qlWHNm/48Vbmo2bGEcBzu1WHgOmgxGo6sh8B6sHsO2MmEXHsgBT6DXbuVwHsLw+nzw3ayZXFh/uD6jH62OvlkOJgQ6ZclKzQaZgwvc/eGpa/DnI4Qc8zsZHZBCnwGWr3nPM3HhRC0+wxDmlVg1fAm1Csvw8GEeGKFK0C/ZcbZNWf+NoaXbQ3M9MPL5JesGSA61hgOtiLyHFWLu/Jjvzp4l8pvdiwhnIuLC/gNgIqtYPmbsOYD2LPIOJe+aFWz05kiLUv2NVRKLXzE4zmVUgOUUh2UUp8rpVws28OUUnMtf8pbM7Sj0FoTtOs0zQNC+HPvBd5tVZmlrzWS4i6ELeUvCb3mQ9cf4MoxmNYEgr+CxHizk2W4VDt4rfUWpdTgR+zSGkjSWi9TSnkANYDdwFSt9VzrxHQ8Z67e5oNFkYQcjKaWhzEczLOozI8RIkMoBd7djIukVo2A4DGwdwl0mgQla5udLsNY4xBNMPDvgeTiwHHL7fpKqYJAJWC41jr5/ida/uEYDODh4WGFKOZLTtbM3X6Cr1btRwOfdvDihQZlyeIi5+gKkeHyFIFuPxjFfvlb8H1zaPAqNPsAsjv/6I80LbqtlJqltX4xlX08gcZa61mW+1W11vuUUv2BY1rr4Ec93xkW3T4SfYORQRH8dfwKTSoWYUxnb0oXcv4PkRAOIe4a/Pkx7JoFBcsZw8vKNTE71RN71KLbVjmLRinlDtS8q7jnBGIsD58GnHrp9ISkZKYEH6bNhE0cOB/LN918mDOgrhR3IexJzvzQYYJxtg3A7PawbLhR+J1Uug7RKKWyASW01ifu2pYdaKu1/tHyuBdQDvAAJgKlgX+sltjO7DlzjRFBEUSdvU7rau7879lqFM0n82OEsFvl/I3z5oPHGKdSHlxrnF5ZubXZyawuLWfR+ANNlFKdgFrApPt2eQlopZSaC2wAEoG1wG2lVEfAVWu927qxzReXkMQ3a/bTKTCMC9fvMLV3Laa9UFuKuxCOIHtuaPm5cSVsrgLwaw9YONBYZMSJpOkYfEZwpGPwO4/H8F5QBEejb9K1VilGta9KgdwyP0YIh5QYD5vHGevA5nSFNl9D9a4OM7zM5sfgM4ubdxL5dGkU3adv5U5CMrMH1OW753yluAvhyLJmh2YjjCmVBctC0ED4tSdcO2N2sicmBT6NQg5G03JcKLO3Hqdfg7KsfdOfppXczI4lhLCWYl7GvPlWY+BoiDG8bOePkPzAGd4OQ0YVpOLqrXhGL99H0O7TlHfLw4KXG+BXtpDZsYQQtuCSxThPvnJbWDbMGHmwZ5Fx9k3hCmanSzfp4B9hVeQ5mgeE8sc/Z3j1qQqsHNZEirsQmUGhctB3KXSYCOfCYWpDCJsISYlmJ0sX6eBTcPF6HB8viWJ11HmqlXBl9oA6VCsh82OEyFSUgtr9oGILWPE2/DkKohYbw8uKVTM7XZpIB38XrTULdp6ieUAIGw5c5L3Wlfnj1UZS3IXIzFxLQM950O1HuHoSpvvDxjGQeMfsZKmSDt7iVMwtPlgcyaZDl6hTtiBju/pQwS2v2bGEEPZAKePUyXLNYM37EPIV7F1qdPOlUjxD0S5k+g4+KVnzU9gxWo0PZfeJK4zuVI35gxtIcRdCPChPYegyA3otgDvXjeFlqz+A+JtmJ0tRpu7gD1+MZURQJLtOXKFpJTe+6FydUgVlfowQIhWVWsLQbbDuU9gWCPuXQ8eJxnhiO5IpO/iEpGQmbzhE2wmbORJ9g4DnfJnVv44UdyFE2uV0hfYB8OJKcMkKczoZa8Levmp2sv9kug5+z5lrvLswgn3nrtPOuzifdqyGW74cZscSQjiqso1gSBgEfwlbJlmGlwVAlXZmJ8s8HXxcQhJjVxnDwS7duMO0PrUJ7F1LirsQ4sllywUt/gcvrTcWGfmtFyzoDzeiTY2VKTr4HcdiGBkUwdFLN+nhV5oP2lYlf+5sZscSQjibkrVgcDCEjYeQr+HoRmj9Ffg8Z8rwMqfu4GPjEhj1xx6em76V+KRk5g6sx1fdfKS4CyFsJ0s28H8XXtkMhSvC4sHwS3e4eirDozhtgd944CKtxoUyd/sJBjQqx9o3/WlcsYjZsYQQmYVbZRiw2ujgT4TBlPrw1/cZOrzM6Q7RXLkZz+jle1n09xk8i+Zl4SsNqV2moNmxhBCZkUsWqP+KsVrUsuHGyIPIIGM92CKetn97m79DBtFaszziLM0DQlgafpZhT3uyYlhjKe5CCPMVLAsv/AGdAuFiFExrBJvH23x4WaodvFKqIfCW1rrbI/YZDlwF8mutJz5sm61cuB7HqD/2sHbvBbxL5mfuS/WoWtzVlm8phBDpoxTU7AOezY1Oft0nELXIKPru3jZ5y1Q7eK31FuDGwx5XSnkCxbXWs4GCSqkqKW2zWuL7bNx/keYBIYQcjOb9NlVYPLShFHchhP3K5w49f4Hn5sD1czCjGWydYpO3ssYx+KeBHZbb4UBTQKewbf/9T1RKDQYGA3h4eDzWm5crkodaHgX5tGM1yhXJ81ivIYQQGc6rE5RtAms+NObP24A1CnwR4Kjl9g2gKqBS2PYArfUMYAYYi24/zpuXLZKH2QPqPs5ThRDCXLkLQeepNnt5axT4y0A+y+18lvsqhW1CCCEyULrOolFKZVNKlblv80agjuW2LxD8kG1CCCEyUKoFXinlDzRRSnUCagGT7n5ca30QuKCU6gfEaK0PprTNBtmFEEI8QqqHaLTWocDdy4l3TGGfCWnZJoQQIuM4zYVOQggh7iUFXgghnJQUeCGEcFJS4IUQwkkprR/r+iKrU0pFAyce8+lFgEtWjGMt9poL7Deb5EofyZU+zpirjNbaLaUH7KbAPwml1E6ttZ/ZOe5nr7nAfrNJrvSRXOmT2XLJIRohhHBSUuCFEMJJOUuBn2F2gIew11xgv9kkV/pIrvTJVLmc4hi8EEKIBzlLBy+EEOI+UuCFEMJJSYEXQggnZY0FP2wuLQt4m7Hwd2qvr5TKCfQCooF6wMda62SlVBhwzLLbx1rro/c/15a5LPs8kMEOvl81gGnAYYwLPyZrrZdnwPfLLheWTy2XiZ+vtHy/zPh8pfb9qkEGf74e9jNKYT+bfL7svoNPywLeZiz8ncbXbw0kaa2XAeeAGpbtU7XWfSx/rP2XL61f9z0Z7OT7lQ3w11r3AeZorZenlNWaucB+F5ZPLRcmfL7SmOuBDHby/TLj8/Wwn9F/bPn5coQOPqVFve9fwPuxF/62ca5goLDldnHguOV2faVUQaASMDylf9FtnOuBDOl4ns1yaa3/AlBKlQSuPCyrlb9faWHG5ystgsn4z1daZfTnK1Umfb6CSflndDebfb7svoPH+K/UdcvtG0ChNO6TlufZNJfW+qrW+ojlX+PDWusYy0OBWutJwG7AP6NzPSSD6d+vu7wArL3rvi2/X2lhxucrVSZ9vtIqoz9f6ZFhn69H/IzuZrPPlyN08Ckt6p2WfWy98HdacqGUcgdqaq1nWe7nBP79IZ8G3DM610MypOnrsWUuSzYFlNNaJz0ia0az24XlTfh8pSWTGZ+vNDHj83X/zygFNvt8OUIHf/8C3iF2svB3qrmUUtmBtlrrBZYFy30xjsn1sOxSGrD2erVp+X6llMH075dFRe5tPGz9/bqHvS4sn1Iukz5fqeZ6SAbTv18WGfr5SulnlJGfL7sv8Pq+BbyBAtjBwt9pyQW8BLRSSs0FNgCJGP81vK2U6gi4aq13m5DrgQx28v0CyAnEPiqrNXOB/S4sn1ouTPh8pTFXhn++0pgLMv7zdf/PqMD9uWz5+ZJRBUII4aTsvoMXQgjxeKTACyGEk5ICL4QQTkoKvBBCOCkp8EII4aSkwAshhJP6P6Sk6fsiAuhuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_up, = plt.plot([1, 2, 3], label='Line 2')\n",
    "line_down, = plt.plot([3, 2, 1], label='Line 1')\n",
    "plt.legend([line_up, line_down], ['Line Up', 'Line Down'],loc=5, title='line',frameon=False)#loc参数设置图例所在的位置，title设置图例的标题，frameon参数将图例边框给去掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2092ead7e50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H9wIy6PMPHGfmfCfYI8JC+NmYFH4yqjvtIjQTp68o4EV8JSQUBt4K/W+EtW85Qf/GjdB1sBP0KeMCIui3HTzBrPn5zFu3l4iwEO5NT+ano5Jo30rB7msaJiniLyorYM0bztQHR7+GuCGQ8WtIvswvg76gyAn2D9buJTwshNtHJDJldBIdFOxepXHwIoHkTDmseR0W/S8c3Q3dhjlBnzTGL4J+x6GTzJ6fz7/W7KFFaAi3j0hgyugkolrr6Ve+oIAXCURnTsPq15ygP7YH4kc4Qd893SdBv6v4JLPmb+Nfa/YQFmK4bXgCd6cnE61g9ykFvEggO3MaVv3VeYTg8b2QcInTR999lFc2/3VxKbMX5PPP1XsIbWaYNDyBu9OT6BipJ435AwW8SDCoKHOCfvHTcHwfJI5ygj7xEo9sbndJKX9asI13VxXSrJnh1mHx3JueTMc2CnZ/ooAXCSYVZbDyFSfoTxxwHhae8SgkjGiU1RceLmVO9jbeyXWC/Zah8dybkUwnBbtfUsCLBKOKU5D7F1j8DJw86DwsPGM6xA93a3V7jpxyBftuDIabhnbjvowUOrdVsPszBbxIMCsvhdyXYUkmnCxyRtuMeRS6DW3Q1/e6gv3vubsBmDjECfYu7Vp6sGhpLAp4kaag/CR89WdYMhNKDznj58c8CnG1/uyz7+gpns3ezttf7cZiuSGtG/ePSaGrgj2gNHrAux6ofQtQBAwDfmOtraql3VTgCNDWWjurrnUq4EUayekT8NVLTtCfKoGU8TBmunOHLHDgWBnPZm/jzRW7qbKWG9LiuH9MCnHtI3xcuLjDE/PBXwFUWmvnGWPigQHAqhobTQFirbUzjTG/Ncb0stZucXN7ItJQLVrDpdNgyE9gxVxYOgteHMvp7uN4tcUt/HFDBJVVlusHxfGzsSl066BgD1buBnwOEOVajgV21tJmLLDCtbwW5yHd3wp4Y8wUYApAfHy8m6WISK1atIZRD1PU53bWv/sUgwpeY4rJ4pL2I+nw/d8S27u/rysUD3PrsTLW2iPW2u2us/Rt1tqSWppFA8dcyyeADrWsZ661Ns1amxYTE+NOKSJyDkXHT/PEvzcxKvMrfrIjnad6/YPDw37JxRUbiX37cnjzFti3ztdlige5PZukMaYzMNBa+8o5mhQDka7lSNdrEfGw4hOneWFhAX9btovTZyq5ZkBXHrisB92jWwEjYczP4MvnYdkceGGU82SpjOnQua+vS5dG5lbAG2OaA9+31r5sjAkD+gBHrLW7zmqWDUwG3gNSgTcusFYRqUPJyXJeWLidvy51gn1CahceuKwHyTGtv90wvK3zyMBhd8OXz8KXz8GWf0PvCc5cN50u9s0OSKNzdxTNfTh96hVAAvA48Ii1dkKNdhpFI+Jhh0+WM3dRAa8u3cmpikqu6t+FBy/rQUrH1vV/GeDUYeds/svnofw49LnGCfqOvT1atzQOjYMXCUJHSst5cVEBryzZSWlFJT/oF8vUy3rQo1Nk/V+uTWkJLPsTLH/BGVN/8bVO0Mdc1LiFS6NSwIsEkaOlFby0uIC/LNnJidNnnGAf14Oe7gZ7TSeLYdlsWD4XKkqdZ8im/wpiejbO+qVRKeBFgsDRUxW8vHgHLy/ewfHTZ7iyb2emjutBr85tPLPBk4ecMfQrXoQzZdD3eifoo1M8sz1xiwJeJIAdK6vgL4t38tLiAo6XneHyizsx9bKe9OnioWCv6UQRLJ0JK16CytPQ70ZI/yVEJXtn+1InBbxIADpeVsErS3by4qICjpWdYXyfTkwb14OLu7T1TUEnDjrTH3z1Z6gsh/4TIf0X0CHJN/UIoIAXCSgnTp/h1aVOsB8prWBc745MG9eTvl19FOw1HT/gzFyZ+7LzoPDUm2H0z6FDd19X1iQp4EUCwMnTZ3h12U5eXFjA4dIKxvbqyLRxPegf187XpdXu+H5nLvrcv4CtdAX9L6B9gq8ra1IU8CJ+rLT8DH9dtou5CwsoOVnOmItimDquJwO6tfN1aQ1zbJ8T9CtfcYJ+wK3OGX07zS/lDQp4ET9UWn6G177cxQtfFFB8spz0njFMG9eDgfHtfV2ae47tdR4MvupVsBYGToJRj0C7br6uLKgp4EX8yKnySl5fvovnv9jOoRPljOoRzbRxPRmcEKDBXtPRQlfQ/9V5Peh2J+jbdvVtXUFKAS/iB8oqKnl9+dc8l7OdQydOc2lKNNPG9SAt8TsTrQaHI7th0f/C6tfAGBh0B4x6GNp08XVlQUUBL+JDZRWVvLnCCfaDx08zMjmKaeN6MrR7kAZ7TUe+hoV/hDWvgwmBwZPh0oegTayvKwsKCngRHyirqOTtr3bzbM42Dhw7zfCkDkwb15PhSVH1fzkYHd7pCvo3ICQMBt/pBH1kJ19XFtAU8CJedPpMJX//ajdzsrez/1gZQxM7MG18D0YmR/u6NP9QssMJ+rVvOkGfdpfziMHWHX1dWUBSwIt4wekzlbyTW8iz2dvYe7SMtIT2PDS+JyOTozDG+Lo8/1O83Qn6dW9BSAsYchdcMg1a6+lu50MBL+JB5WeqeGflbuYscIJ9UHw7Hhrfk0tTohXsDVG8Hb54Ctb/HULDnYeFXzIVWuk3noZQwIt4QEVlFf9YWcifFmxjz5FTDOjmBPvoHgp2txzKdwX9OxAWAUN/CiMfhFZN9JpFAyngRRpRRWUV/1xVyOwF2yg8fIrUuLZMG9+TjJ4xCvbGUJQHX/wBNrwLzVvB0Ckw8gGIaCKjjs6TAl6kEZyprOKfq/fwpwXb+LqklP5xbZk2rgdjLuqoYPeEg1ucoN/4HjRv7TxDdsT9CvoaGj3gjTEjgYettdfX0WYJsMP18jfW2oK61qmAF391prKKf63Zy+wF+ewqLqVv1zZMu6wnl/VWsHvFgU1O0G/6F7RoA8PugRH3QcsgufP3AnnkDN4Y84q1dnIdn0+y1r7W0PUp4MXfVFZZ3l+zh9kLtrHj0En6xLZh2rgejO/TScHuCwc2Qs7vYfMH0KItDL/X+dOyna8r86m6Aj7Ug9sdboxpD/QEplprq2opbAowBSA+XjPPiX+orLLMW7uXWfPzKTh0kl6dI3l+0mAuv1jB7lOdLoaJf4P9652g/+L3sPw5GH4/DL8Hwv1kvnw/4skz+N7W2s3GmDuBHdbanLrWpzN48bXKKsu/1znBvr3oJBd1imTauB5cfnFnmjVTsPudfWsh5w+w9UMIbwcjfub004d76VGGfsLrZ/DGmHCgxPWyEOjsie2INIaqKsuH6/cxa34++QdP0LNTa+bcMogr+yrY/VpsKtz8Buxd45zRZz8BX875T9C3iPR1hT53wQFvjAkDulhrd5319hVAPDAL6AasudDtiDS2qirLxxv2M3N+HnkHTpDSsTWzbx7ID/rFKtgDSZcBcMtbsGeVE/QLfgfL5jhDK4dOgRatfV2hz7g7imY08BfgYWA/8Ji1dsJZn0cAtwIHgCRrbWZ961QXjXhLVZXl0437mTk/ny37j5Mc04oHL+vBD/t3IUTBHvgKV0LODNj2OUREOTdLDf2pM6Y+CGkcvAhgreXTjQeYOT+fzfuOkRTtBPtVqQr2oLT7Kyfot8+HiGhn+oMhP4HmEb6urFEp4KVJs9by+aYDZGbls2nfMRKjInjwsh5MSO1CaEgzX5cnnvb1cifoC7KhVYwzoVnaj4Mm6BXw0iRZa5m/+SCZ8/PYsOcYCVERPDC2B9cMULA3SbuWOUG/4wto3ckV9HdCWEtfV3ZBFPDSpFhryd56kMysfNYVHqVbh5Y8MLYHPxrYVcEusHOJE/Q7F0Hrzs5DRwZPhrBwX1fmFgW8NAnWWnLyisjMymft7iPEtW/Jg2N7cO2groQp2KWmHYucUTe7FkNkLFz6sPOA8AALegW8BDVrLQvzD5GZlcfqr4/QtV1LHhibwnWD4xTsUr8dCyF7Bny9FCK7OA8GH3Q7hLbwdWUNooCXoGStZfG2Q2Rm5bNy12G6tmvJ/WNSuH5wHM1DFexyHqx1+uazZ8DuL6FNnBP0A2+D0Oa+rq5OCngJKtZalm4vJjMrj692Hia2bTj3j0nhhrQ4WoSG+Lo8CWTWOqNtsmdA4Qpo2w1GPQIDbvXboFfAS9BYtr2YZ7LyWLGjhM5twrl/TDI3DummYJfGZa0zfj57BuzJhXbxMOrnMOAW50HhfkQBLwFveYET7F8WlNCpTQvuy0hh4pBuhIcp2MWDrIVtWZD9JOxdBe0SYPQvIPUmvwl6BbwErK92lvDM53ks3V5MTGQL7stI5uah8Qp28S5rIf8zJ+j3rYH23Z2g7z8RQjw563r9FPAScHJ3lpCZlc/ibYeIbt2CezOSuXWYgl18zFrI+8QJ+v3roEMSjP4l9LvBZ0GvgJeAsXLXYTKz8liUf4jo1s25Jz2ZW4cl0LK5gl38iLWw9SPnhqn966FDMqT/CvpdD828+/+qAl783uqvD/NMVj4L84qIatWcu9OTmDQ8gYjmvv31V6ROVVXOA0dyfg8HNkBUDyfo+/7Ia0GvgBe/tXb3EZ7JyiNnaxHtI8K4Oz2Z24Yn0KqFgl0CSFUVbJnnBP3BTRB9EaT/Ei6+1uNBr4AXv7O+8CjPZOWxYMtB2kWEMWV0EneMSFSwS2CrqoLN7zuPEizaDDG9nDP6PtdAM8/cfKeAF7+xYc9RMrPyyNp8kLYtXcE+MpHWCnYJJlVVsOk9J+gPbYWOfZyg7z2h0YPe689kFalp496jZGbl8/mmA7QJD+WR8T2ZfEkikeH+MZZYpFE1awZ9r3PO3De+53TdvHMHdOrrBH2vH3rsjP5sCnjxqM37jpGZlcenGw8QGR7KQ+N6cuelibRRsEtT0CzEGVlz8bWw4V344g/w99ugUz/I+DX0+gEYzz1NzK2AN8aMBB621l5fR5upwBGgrbV2lnvlSaDasv8YM7Py+XjDfiJbhPLgZT2469LutG2pYJcmqFkI9L8RLv4RbPiHE/Rv3wqd+0PGdLjoSo8EvVu/I1hrlwInzvW5MSYFiLXWvgq0N8b0crM+CUDzNx/gisxFLMo/xANjU1j8q7E8PL6nwl0kJNSZ5uD+r+Ca5+D0cXjrZvhkukc256kumrHACtfyWiAd2FKzkTFmCjAFID4+3kOliLddkhLNI+N7ctuIBNpF+OcMfCI+FRLqTFzW7wZY97bTN+8BnurljwaOuZZPAB1qa2StnWutTbPWpsXExHioFPG28LAQHrish8JdpD4hYTBwEnQZ4JHVeyrgi4FI13Kk67WIiHjRBQe8MSbMGJNQ4+1sYIhrORXIudDtiIjI+XEr4I0xo4FRxpirgUHA7LM/t9bmAQeMMXcAJa7XIiLiRW5dZLXWLgSSz3prQi1tZrpblIiIXDg9mVhEJEgp4EVEgpQCXkQkSCngRUSClAJeRCRIKeBFRIKUAl5EJEgp4EVEgpQCXkQkSCngRUSClAJeRCRIKeBFRIKUAl5EJEgp4EVEgpQCXkQkSCngRUSClAJeRCRIufVEJwBjzFTgCNDWWjvrHG2WADtcL39jrS1wd3siInJ+3H0mawoQa619FWhvjOl1jqbPWWsnuf4o3EVEvMjdLpqxwArX8log/RzthhtjHjDGzDbGfGdbxpgpxphcY0xuUVGRm6WIiEht3A34aOCYa/kE0OEc7eZYa2cDq4DRNT+01s611qZZa9NiYmLcLEVERGrjbsAXA5Gu5UjX628xxoQDJa6XhUBnN7clIiJucDfgs4EhruVU4AtjTEKNNlcAE13L3YA8N7clIiJucCvgrbV5wAFjzB04Z+ntgNk1mn0GnDLGTADaWGtXXUihIiJyftweJmmtnVnjrQk1Pi8FXnR3/SIicmF0o5OISJBSwIuIBCkFvIhIkFLAi4gEKQW8iEiQUsCLiAQpBbyISJBSwIuIBCkFvIhIkFLAi4gEKQW8iEiQUsCLiAQpBbyISJBSwIuIBCkFvIhIkFLAi4gEKQW8iEiQUsCLiAQptx/ZZ4yZChwB2lprZ7nbRkREPMOtM3hjTAoQa619FWhvjOnlThsREfEcd7toxgIrXMtrgXR32hhjphhjco0xuUVFRW6WIiIitXE34KOBY67lE0AHd9pYa+daa9OstWkxMTFuliIiIrVxN+CLgUjXcqTrtTttRETEQ9wN+GxgiGs5FfjCGJNQT5scN7clIiJucCvgrbV5wAFjzB1ACdAOmF1XG9frpqugwNcViEgT4/YwSWvtzBpvTWhAm6Zpxgx49FF48kmYPt3X1YhIE6EbnTxtxgx44gln+YknnNciIl6ggPek6nAvLXVel5Yq5EXEaxTwnlIz3Ksp5EXESxTwnlBQ4PS51wz3aqWlzue68CoiHqSA94SkJOeCakRE7Z9HRDifJyV5ty4RaVIU8J4yfTo8/vh3Qz4iwnlfo2lExMMU8J5UM+QV7iLiRW6Pg5cGqg7zRx9VuIuIVyngvWH6dJg4UX3uIuJV6qLxFoW7iHiZAl5EJEgp4EVEgpT64EXE71VUVFBYWEhZWZmvS/GZ8PBw4uLiCAsLa/B3FPAi4vcKCwuJjIwkMTERY4yvy/E6ay3FxcUUFhbSvXv3Bn9PXTQi4vfKysqIiopqkuEOYIwhKirqvH+DUcCLSEBoquFezZ39V8CLSPBq4hP6uRXwxpipxpg7jDEP1tNuiTHmNdcfDQQXEe+ZMQOSkxttau7jx48za9asOtucPn2ao0ePNmh9RUVFzJ07tzFKO6fzDnhjTAoQa619FWhvjOlVR/PnrLWTXH+a9j+lIuI9HniSWm5uLps3b66zzQsvvMDq1avrXVdFRQWrV69m69atF1xXXdw5gx8LrHAtrwXS62g73BjzgDFmtjFG3UEi4nkeepLamDFj6NSpU51t+vfv36B1hYWF8b3vfY/IyMgLqqk+9Q6TNMaMAxLPeisGqD4bPwH0ruPrc6y1m40xdwKjgZwa654CTAGIj49vcNEiIrWq70lq0GgT/hUUFPDZZ5/Rq1cvtm/fzl133QXAqlWrKC4uZtWqVfz3f/83xhjmzJlDfHw8e/fu5d57722U7TdEvWfV1tosa+1L1X+AQ0D1PzuRQHFt3zPGhAMlrpeFQOda1j3XWptmrU2LiYlxawdERACvP0mttLSUK6+8koEDB7J+/fpv3u/YsSPXXXcdffr0YfHixXzwwQf07duXq6++mpKSEvbt29co228Id7pNsoEhruVUIMcYE2aMSajR7gpgomu5G5DnXokiIg3g5SeptW/fntdee421a9fSokWLb96Pi4sDnKDft28fW7duZe/eveTk5NCxY0dOnTrVKNtviPMOeGttHnDAGHMHUOJ6PQiYXaPpZ8ApY8wEoI21dtUFVysiUhcvPknt6aef5t5772X06NG0bNmSM2fOAM4FVID9+/eTlJREcnIyXbt2JSMjg+uuu46uXbs2Wg31cWuqAmvtzBqvlwMTarxXCrzofmkNV3C4gKT2GoUpIvwnxKv74hsp3HNycli3bh05OTlkZGSQkZHB3/72N5KSkigpKWH+/PlERESwYcMGysvLOXr0KEOHDmXw4ME89dRT7N+/H4CbbrqJsrIyli1bxvr16/nyyy8ZPnz4he51rYy11iMrPl9paWk2Nzf3vL83Y9EMHl3wKE+OfZLpo/S0JJFgtHnzZnr3rms8Ry1mzHD63J98MmiepFbb34MxZqW1Nq229gE92diMRTN4YpFzZbz6vwp5EQH0JDUCeKqC6nAvrXCumJdWlPLEoieYsahx7lqTRtDEbxMXP9CEwx0CNOBrhns1hbwfaeTbxEXk/AVcwBccLuDRBY9+J9yrlVaU8uiCRyk4rLNHn/HAbeIicv4CLuCT2ifx5NgniQirfaxrRFgET459UqNqfMVDt4mLuKOpn+gFXMCDcyH18VGPfyfkI8IieHzU47rQ6iv13SaukBcvmrFoBsmzkhuty7ZJzCbpL2qGvMLdx7x8m7hIXWqOsGuMkPfGbJKffvopkydPvpAyvyVgAx7+E/KAX4R7k/510Mu3iYuci6dG2HljNsnLL7/c7fpqE9ABD07Ib39wu8/DvbF/HQxIXrxNXKQ23hxhV1BQwPPPP09OTg5//vOfv3l/1apVvPvuuzz22GNUVlZSVVXF7Nmzef/993nuuecabfsNEfABD/j8gqonfh0MWDVDXuEuXuLtEXbBOpuknEU3XNWiOuRB4S5e4+0RdoEwm2RAT1Xga/X9OghNeOoE3SYuPlD981bz59ITgzCefvppHnvsMTp06MCCBQtqnU3yoosuIjQ0lOjoaDIyMujfvz+tWrVqtBrqozN4N+mGqwZQuIsPeGqE3dmzSQLfzCY5b968b2aTDAkJYevWrXz44YffzCZ57bXXsmzZMt566y0+++wzWrRoQVlZGdnZ2d/MJllt3rx5LFq0iOzs7AuqtVrAzybpS+c6gwcN2xRpTO7MJhmMM802qdkkfc2bvw6KyPmZPmo6E/tO9PkgDF9SF80F0g1XIt7hTm9DMIW7O/uvgG8E/nbDlUiwCQ8Pp7i42K2QCwbWWoqLiwkPDz+v77nVB2+MGQk8bK29vp52U4EjQFtrbZ2TOARiH3xNenSgiGdUVFRQWFhIWVmZr0vxmfDwcOLi4ggLC/vW+43eB2+tXWqMmVJXG2NMChBrrZ1pjPmtMaaXtXaLO9sLFAp3Ec8ICwuje/fuvi4j4Hiyi2YssMK1vBZIr9nAGDPFGJNrjMktKiryYCkiIk1PvWfwxphxQOJZb2231jZkkGY0UD0I/ATwnTFO1tq5wFxwumgasE4REWmgegPeWpvl5rqLgeqp0iJdr0VExEsaZRy8MSYM6GKt3XXW29nAZOA9IBV4o651rFy58pAxZlddbeoQDRxy87v+Jlj2JVj2A7Qv/kr74kg41wduBbwxZjQwyhhzNfABMAh4DJhQ3cZam2eMOWCMuQMosdbm1bVOa22MO7W46sk911XkQBMs+xIs+wHaF3+lfamfu6NoFgLJZ721nLPC/ax2M92sS0RELpBudBIRCVLBEvCefXKtdwXLvgTLfoD2xV9pX+rhN7NJiohI4wqWM3gREalBAS8iEqQU8CIiQSrgHvjhiZksfaWhNRpjlgA7XC9/Y631m+cANmQfAuFYQIP3xW+Pxdka8nMSQMelIfvi98fFGBMO3AIUAcNw6qyqpV2jHZeAO4O31i7FmdvmnM6ayfJVoL0xppdXijsP51njc9baSa4/fvM/bkP2IRCOBZxXnX55LGqq7+ckUI4LNOxnnsA4LlcAldbaecA+YEDNBo19XAIu4Buo3pks/cD51DjcGPOAMWa2McafjllD9iEQjgU0vE5/PRbnK1COS0MFwnHJARa7lmOBnbW0adTj4tddNJ6cydLbatmXGBpe4xxr7WZjzJ3AaJz/UfxBQ/6e/e5YnEND6/TXY3G+AuW4NJTfHxdr7RHgiOssfZu1tqSWZo16XPw64INpJsua+2KMuZsG1Ojqt6v+H6EQ6OypGt3QkL9nvzsW51BvnX5+LM5XoByXegXScTHGdAYGWmtfOUeTRj0u/vqrTIMZY8KMMTVnU8sGhriWU/HDf82ppcZz7MsVwETXcjegzknbvKzmPnwRoMcCGrYv/nwszimAf0a+IwB/Rr5hjGkOfN9a+45rP1I9fVwCLuDPnsnSGGNwZrKcfXYb18yVDZ7J0hfOUeN39gX4DDhljJkAtLHWrvJyqedUcx+AdgTgsYCG7Qt+fCxqqjHja0D+jFSrb18InOPyE+ByY8xrwAK88POiqQpERIJUwJ3Bi4hIwyjgRUSClAJeRCRIKeBFRIKUAl5EJEgp4EVEgtT/B1Zt4aR91vIRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#这个案例是显示多图例legend\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.random.uniform(-1, 1, 4)\n",
    "y = np.random.uniform(-1, 1, 4)\n",
    "p1, = plt.plot([1,2,3])\n",
    "p2, = plt.plot([3,2,1])\n",
    "l1 = plt.legend([p2, p1], [\"line 2\", \"line 1\"], loc='upper left')\n",
    " \n",
    "p3 = plt.scatter(x[0:2], y[0:2], marker = 'D', color='r')\n",
    "p4 = plt.scatter(x[2:], y[2:], marker = 'D', color='g')\n",
    "# 下面这行代码由于添加了新的legend，所以会将l1从legend中给移除\n",
    "plt.legend([p3, p4], ['label', 'label1'], loc='lower right', scatterpoints=1)\n",
    "# 为了保留之前的l1这个legend，所以必须要通过plt.gca()获得当前的axes，然后将l1作为单独的artist\n",
    "plt.gca().add_artist(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1, = plt.plot([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "1.尝试在一张图中运用所讲过的功能，对title、text、xlable、ylabel、数学表达式、tick and ticklabel、legend进行详细的设计.  \n",
    "\n",
    "2.阅读你可能用到文献或者相关书籍，思考自己如何才能通过学过的例子将自己认为比较好看的图给复现出来."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']    # 指定默认字体为雅黑。\n",
    "\n",
    "# 心形函数\n",
    "x = np.linspace(-3.3**0.5, 3.3**0.5, 6001).reshape(-1,1)\n",
    "y = (x**2)**(1/3) + 0.9*np.sqrt(3.3 - x**2)*np.sin(40*np.pi*x)\n",
    "p0 = plt.plot(x,y,color = 'r')\n",
    "plt.xlim(-3,3)\n",
    "\n",
    "p1 = plt.plot([1,2,3])\n",
    "plt.title('心')\n",
    "plt.xlabel('X_label')\n",
    "plt.ylabel('Y_label')\n",
    "plt.legend(p0, 'h', loc='lower right')\n",
    "\n",
    "plt.text(-0.5, 2.5, r'$E=mc^2$', fontsize=15)\n",
    "\n",
    "# 使用axis的set_ticklabels方法手动设置标签格式的例子\n",
    "ticks = np.arange(-3.0, 3.1, 0.5)\n",
    "tickla = [f'{tick:1.2f}' for tick in ticks]\n",
    "plt.xticks(ticks=ticks, labels=tickla, rotation=15)\n",
    "plt.show()"
   ]
  }
 ],
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